Towards Efficient Management of Wireless Sensor Networks

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Towards Efficient Management of Wireless Sensor Networks"

By

Mr. Xin MIAO


Abstract

Motivated by the needs of precise carbon emission measurement and 
real-time surveillance for CO2 management in forests and cities, we 
present CitySee, a real-time CO2-monitoring system using sensor networks 
for an urban area (around 100 square kilometers). In order to conduct 
environment monitoring in a real-time and long-term manner, CitySee has to 
address management issues such as sensor deployment and data processing. 
In this proposal, we aim at studying several fundamental challenges in 
managing large-scale sensor networks, including sensor deployment, node 
diagnosis, network management and link monitoring.

We first investigate the sensor deployment problem. In CitySee, it can be 
abstracted as a relay node placement problem under hole-constraint. By 
carefully taking all constraints and real deployment situations into 
account, we propose an efficient approach which uses additional relay 
nodes at most twice of the minimum. We then study the node diagnosis 
problem and propose a novel approach AD which performs diagnosis in an 
agnostic manner. Specifically, AD does not require network operators to 
predefine the types and symptoms of possible faults. Instead, it explores 
the correlation patterns of system metrics and discover potential faults 
by tracking changes and anomalies of correlation patterns. We further 
study management center placement schemes to improve the performance of 
online network management services based on the quality of interactive 
communications. We define the reachability from a management center to a 
sensor node using Expected Transmission Ratio (ETR) and then design 
optimal and heuristic algorithms in which multiple management centers work 
in a cooperative manner to cover as many sensor nodes as possible. 
Finally, we exploit the sparse property of link loss rates and advocate a 
Compressive Sensing based approach to monitor link qualities using mobile 
sinks. On the identification of lossy links, further management approaches 
can be applied to enhance network performance.


Date:			Thursday, 20 June 2013

Time:			2:00pm – 4:00pm

Venue:			Room 3501
 			Lifts 25/26

Chairman:		Prof. Bilian Ni Sullivan (MGMT)

Committee Members:	Prof. Yunhao Liu (Supervisor)
 			Prof. Lei Chen
 			Prof. Ke Yi
 			Prof. Xiangtong Qi (IELM)
                        Prof. Jiannong Cao (Comp., PolyU)


**** ALL are Welcome ****